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    <title>CUST 机器学习, 2025 秋</title>

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                    <span class="title">CUST 机器学习日程表</span>
                    <h3>2025 秋</h3>
                </td>
            </tr> 
        </table>
        <br>
        
        <table class="schedule" align="center" border="1" width="1022">
            <tbody>
                <tr>
                    <td align="center" width="100"><strong>时间</strong></td>
                    <td align="center" width="100"><strong>地点</strong></td>
                    <td align="center" width="132"><strong>机器学习</strong></td>
                    <td align="center" width="160"><strong>前沿知识讲座</strong></td>
                    <td align="center" width="250"><strong>参考资料</strong></td>
                    <td align="center" width="120"><strong>附件</strong></td>
                    <td align="center" width="160"><strong>报告人</strong></td>
                </tr>

                <tr>
                    <td colspan="7" class="schedule_week" align="center" height="28" valign="middle">第 2 周 监督学习</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">2025.9.16, 18:00</td>
                    <td align="left">实训楼 310</td>
                    <td align="left">线性回归、逻辑回归、K 近邻分类</td>
                    <td align="left">\</td>
                    <td align="left">
                        <b>参考资料:</b>
                        <ol style="padding-left: 20px;">
                            <li>
                                机器学习 3.2、3.3、3.4、10.1
                            </li>
                            <li>
                                统计学习方法 6.1, 第 3 章
                            </li>
                            <li>
                                UFLDL Tutorial Linear Regression
                                <a href="http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/">
                                    [Blog]
                                </a>
                            </li>
                            <li>
                                UFLDL Tutorial Logistic Regression
                                <a href="http://ufldl.stanford.edu/tutorial/supervised/LogisticRegression/">
                                    [Blog]
                                </a>
                            </li>
                            <li>
                                Code
                                <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/Logistic_and_maximum_entropy_models/logisticRegression.py">
                                    [Dod-o LogisticRegression,
                                </a>
                                <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/KNN/KNN.py">
                                    Dod-o KNN,
                                </a>
                                <a href="https://zh-v2.d2l.ai/chapter_linear-networks/linear-regression-scratch.html">
                                    线性回归,
                                </a>
                                <a href="https://zh-v2.d2l.ai/chapter_linear-networks/softmax-regression.html">
                                    Softmax 回归],
                                </a>
                            </li>
                    </td>
                    <td align="center"><a href="assets/pdfs/lec1_intro.pdf">slides</a></td>
                    <td> 物联网应用技术实验室：杨凯程，张译文</td>
                </tr>

                <tr>
                    <td colspan="7" class="schedule_week" align="center" height="28" valign="middle">第 3 周 监督学习</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">2025.9.25, 18:00</td>
                    <td align="left">实训楼 310</td>
                    <td align="left">支持向量机、决策树</td>
                    <td align="left">\</td>
                    <td align="left">
                        <b>参考资料:</b>
                        <ol style="padding-left: 20px;">
                                <li>
                                    机器学习第 4, 6 章
                                </li>
                                <li>
                                    统计学习方法第 5, 7 章
                                </li>
                                <li>
                                    浙大 胡浩基 支持向量机
                                    <a href="https://www.bilibili.com/video/BV1jt4y1E7BQ/?spm_id_from=333.337.search-card.all.click&vd_source=721ac4481300b04ca5abec3fd5c3fd0c">
                                    [课程]
                                </a>
                                </li>
                                <li>
                                    Code
                                    <a href="https://github.com/TheAlgorithms/Python/blob/master/machine_learning/decision_tree.py">
                                        [TheAlgorithms 决策树,
                                    </a>
                                    <a href="https://github.com/TheAlgorithms/Python/blob/master/machine_learning/support_vector_machines.py">
                                        TheAlgorithms 支持向量机,
                                    </a>
                                    <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/DecisionTree/DecisionTree.py">
                                        Dod-o 决策树,
                                    </a>
                                    <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/SVM/SVM.py">
                                        Dod-o 支持向量机]
                                    </a>
                                </li>
                        </ol>
                    </td>
                    <td align="center"><a href="assets/pdfs/lec1_intro.pdf">slides</a></td>
                    <td>智能网络与信息安全实验室：黄毅，马福涛，曾庆吉</td>
                </tr>

                <tr>
                    <td colspan="7" class="schedule_week" align="center" height="28" valign="middle">第 4 周 监督学习</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">2025.10.9, 18:00</td>
                    <td align="left">实训楼 310</td>
                    <td align="left">朴素贝叶斯、EM 算法</td>
                    <td align="left">医学影像计算工程实验室，遥感技术与大数据分析实验室，脑信息与神经康复实验室</td>
                    <td align="left">
                        <b>参考资料:</b>
                        <ol style="padding-left: 20px;">
                            <li>
                                机器学习 7.3, 7.6
                            </li>
                            <li>
                                统计学习方法第 4, 9 章
                            </li>
                            <li>
                                Code
                                <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/NaiveBayes/NaiveBayes.py">
                                    [Dod-o 朴素贝叶斯,
                                </a>
                                <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/EM/EM.py">
                                    Dod-o EM 算法]
                                </a>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="assets/pdfs/lec1_intro.pdf">slides</a></td>
                    <td>智能网络与信息安全实验室：朱玉臣，何宇，赵丹</td>
                </tr>

                <tr>
                    <td colspan="7" class="schedule_week" align="center" height="28" valign="middle">第 5 周 监督学习</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">2025.10.16, 18:00</td>
                    <td align="left">实训楼 310</td>
                    <td align="left">隐马尔可夫模型</td>
                    <td align="left">机器视觉与机器人实验室，三维图形与仿真研究室，大数据科学与工程实验室</td>
                    <td align="left">
                        <b>参考资料:</b>
                        <ol style="padding-left: 20px;">
                            <li>
                                机器学习 14.1
                            </li>
                            <li>
                                统计学习方法第 10 章
                            </li>
                            <li>
                                Code
                                <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/HMM/HMM.py">
                                    [Dod-o 隐马尔可夫模型,
                                </a>
                                <a href="https://github.com/hmmlearn/hmmlearn/blob/main/src/hmmlearn/hmm.py">
                                    hmmlearn 隐马尔可夫模型]
                                </a>
                            </li>
                            
                        </ol>
                    </td>
                    <td align="center"><a href="assets/pdfs/lec1_intro.pdf">slides</a></td>
                    <td>智能网络与信息安全实验室：张国莹，范明俊，卜磊</td>
                </tr>

                <tr>
                    <td colspan="7" class="schedule_week" align="center" height="28" valign="middle">第 6 周 监督学习</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">2025.10.23, 18:00</td>
                    <td align="left">实训楼 310</td>
                    <td align="left">条件随机场</td>
                    <td align="left">量子信息实验室</td>
                    <td align="left">
                        <b>参考资料:</b>
                        <ol style="padding-left: 20px;">
                            <li>
                                机器学习 14.2
                            </li>
                            <li>
                                统计学习方法第 11 章
                            </li>
                            <li>
                                Code
                                <a href="https://github.com/kmkurn/pytorch-crf">
                                    [crf]
                                </a>
                            </li>

                        </ol>
                    </td>
                    <td align="center"><a href="assets/pdfs/lec1_intro.pdf">slides</a></td>
                    <td>智能决策实验室：刘佳琪</td>
                </tr>
            
                <tr>
                    <td colspan="7" class="schedule_week" align="center" height="28" valign="middle">第 7 周 无监督学习</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">2025.10.30, 18:00</td>
                    <td align="left">实训楼 310</td>
                    <td align="left">K-means 聚类、主成分分析、T-SNE</td>
                    <td align="left">物联网应用技术研究室，机器学习与数据挖掘实验室</td>
                    <td align="left">
                        <b>参考资料:</b>
                        <ol style="padding-left: 20px;">
                            <li>
                                机器学习第 9 章
                            </li>
                            <li>
                                统计学习方法第 14 章
                            </li>
                            <li>
                                机器学习 12.2
                            </li>
                            <li>
                                统计学习方法第 16 章
                            </li>
                            <li>
                                Stanford PCAWhitening
                                <a href="http://ufldl.stanford.edu/tutorial/unsupervised/PCAWhitening/">
                                    [课程]
                                </a>
                            </li>
                            <li>
                                Code
                                <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/Clustering/K-means_Clustering/K-means_Clustering.py">
                                    [Dod-o K-means_Clustering,
                                </a>
                                <a href="https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_means_clust.py">
                                    TheAlgorithms K-means_Clustering,
                                </a>
                                <a href="https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/PCA/PCA.py">
                                    Dod-o PCA,
                                </a>
                                <a href="https://github.com/pavlin-policar/openTSNE/blob/master/openTSNE/tsne.py">
                                    T-SNE]
                                </a>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="assets/pdfs/lec1_intro.pdf">slides</a></td>
                    <td>遥感技术与大数据分析：张学谦</td>
                </tr>

                <tr>
                    <td colspan="7" class="schedule_week" align="center" height="28" valign="middle">第 8 周 自监督学习</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">2025.11.6, 18:00</td>
                    <td align="left">实训楼 310</td>
                    <td align="left">Contrastive Learning、Masked Image Modeling</td>
                    <td align="left">数字媒体与虚拟现实研究室：尹泽楷，董书丞，周星雨</td>
                    <td align="left">
                        <b>参考资料:</b><br>
                        <ol style="padding-left: 20px;">
                            <li>
                                Oord A, Li Y, Vinyals O. Representation learning with contrastive predictive coding[J]. arXiv preprint arXiv:1807.03748, 2018.
                                <a href="https://arxiv.org/pdf/1807.03748.pdf?fbclid=IwAR2G_jEkb54YSIvN0uY7JbW9kfhogUq9KhKrmHuXPi34KYOE8L5LD1RGPTo">
                                    [PDF]
                                </a><br>
                            </li>
                            <li>
                                He K, Fan H, Wu Y, et al. Momentum contrast for unsupervised visual representation learning[C] CVPR 2020.
                                <a href="https://openaccess.thecvf.com/content_CVPR_2020/papers/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.pdf">
                                    [PDF]
                                </a><br>
                            </li>
                            <li>
                                He K, Chen X, Xie S, et al. Masked autoencoders are scalable vision learners[C] CVPR. 2022.
                                <a href="https://link.zhihu.com/?target=https%3A//arxiv.org/abs/2111.06377">
                                    [PDF]
                                </a><br>
                            </li>
                            <li>
                                Self-Supervised Representation Learning.
                                <a href="https://lilianweng.github.io/posts/2019-11-10-self-supervised/">
                                    [Blog]
                                </a><br>
                            </li>
                            <li>
                                Contrastive Representation Learning.
                                <a href="https://lilianweng.github.io/posts/2021-05-31-contrastive/">
                                    [Blog]
                                </a><br>
                            </li>
                            <li>
                                Code
                                <a href="https://colab.research.google.com/github/facebookresearch/moco/blob/colab-notebook/colab/moco_cifar10_demo.ipynb">
                                    [Moco CIFAR-10 Demo]
                                </a><br>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="./ssl.ipynb">slides</a></td>
                    <td>大数据科学与工程：王迪，
                        机器学习与数据挖掘：翟宁</td>
                </tr>
        
                <tr>
                    <td colspan="7" class="schedule_week" align="center" height="28" valign="middle">第 9 周 现代神经网络</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">2025.11.13, 18:00</td>
                    <td align="left">实训楼 310</td>
                    <td align="left">线性层、卷积操作、自注意力机制</td>
                    <td align="left">数字媒体与虚拟现实研究室：白越，曲恃正，田霖，吕伶</td>
                    <td align="left">
                        <b>参考资料:</b><br>
                        <ol style="padding-left: 20px;">
                            <li>
                                <a href="https://zh-v2.d2l.ai/chapter_convolutional-neural-networks/index.html">
                                    动手学深度学习
                                </a><br>
                            </li>
                            <li>
                                Code
                                <a href="./basic_layers/">
                                    [深度网络基础层总结与基础向量操作]
                                </a><br>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="./basic_layer.ipynb">slides</a></td>
                    <td>数字媒体与虚拟现实：白越，周星雨</td>
                </tr>

                <tr>
                    <td colspan="7" class="schedule_week" align="center" height="28" valign="middle">第 10 周 现代神经网络</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">2025.11.20, 18:00</td>
                    <td align="left">实训楼 310</td>
                    <td align="left">BP 算法</td>
                    <td align="left">刘佳琪，黄毅</td>
                    <td align="left">
                        <b>参考资料:</b><br>
                        <ol style="padding-left: 20px;">
                            <li>
                                The spelled-out intro to neural networks and backpropagation: building micrograd
                                <a href="https://www.youtube.com/watch?v=VMj-3S1tku0">
                                [课程] 
                                </a><br>
                            </li>
                            <li>
                                Automatic Differentiation
                                <a href="https://www.youtube.com/watch?v=56WUlMEeAuA">
                                [课程]
                                </a><br>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="./bp.ipynb">slides</a></td>
                    <td>量子信息实验室：于依桐，王千通</td>
                </tr>

                <tr>
                    <td colspan="7" class="schedule_week" align="center" height="28" valign="middle">第 11 周 现代神经网络</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">2025.11.27, 18:00</td>
                    <td align="left">实训楼 310</td>
                    <td align="left">VGG、ResNet、InceptionNet、ReplkNet、Vision Transformer、Swin Transformer</td>
                    <td align="left">⻢福涛，曾庆吉</td>
                    <td align="left">
                        <b>参考资料:</b><br>
                        <ol style="padding-left: 20px;">
                            <li>
                                Code
                                <a href="./networks/">
                                    [神经网络总结]
                                </a><br>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="./basic_net.ipynb">slides</a></td>
                    <td>量子信息实验室：段羽，王澈</td>
                </tr>

                <tr>
                    <td colspan="7" class="schedule_week" align="center" height="28" valign="middle">第 12 周 生成式 AI</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">2025.12.4, 18:00</td>
                    <td align="left">实训楼 310</td>
                    <td align="left">自回归编码器、对抗生成网络、DDPM (DDPM 单独报告)</td>
                    <td align="left">赵丹，朱玉臣</td>
                    <td align="left">
                        <b>参考资料:</b><br>
                        <ol style="padding-left: 20px;">
                            <li>
                                MIT-6.S978 深度生成模型 2024 年秋季
                                <a href="https://mit-6s978.github.io/schedule.html">
                                    [课程]
                                </a><br>
                            </li>
                            <li>
                                NTU 2023 年春季
                                <a href="https://speech.ee.ntu.edu.tw/~hylee/ml/2023-spring.php">
                                    [课程]
                                </a><br>
                            </li>
                            <li>
                                Auto-Encoding Variational Bayes (VAE) (arXiv, 2013)
                            </li>
                            <li>
                                Neural Discrete Representation Learning (VQ-VAE, NeurIPS 2017)
                            </li>
                            <li>
                                Generative Adversarial Nets (NeurIPS 2014)
                            </li>
                            <li>
                                Score-Based Generative Modeling through Stochastic Differential Equations (arXiv 2020)
                            </li>
                            <li>
                                Code
                                <a href="https://nn.labml.ai/gan/index.html">
                                    [GAN, 
                                </a><br>
                                <a href="https://nn.labml.ai/diffusion/index.html">
                                    Diffusion Model]
                                </a><br>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="./auto_encoder.ipynb">slides</a></td>
                    <td>三维图形与仿真：杨晨，机器视觉与机器人：田家琪</td>
                </tr>

                <tr>
                    <td colspan="7" class="schedule_week" align="center" height="28" valign="middle">第 13 周 生成式 AI</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">2025.12.11, 16:00</td>
                    <td align="left">实训楼 310</td>
                    <td align="left">扩散模型，Flow matching</td>
                    <td align="left">范明俊，张国莹</td>
                    <td align="left">
                        <b>参考资料:</b><br>
                        <ol style="padding-left: 20px;">
                            <li>
                                LDM: High-Resolution Image Synthesis with Latent Diffusion Models (CVPR 2022)
                            </li>
                            <li>
                                DDPM: Denoising Diffusion Probabilistic Models (arXiv 2022)
                            </li>
                            <li>
                                Scalable Diffusion Models with Transformers (DiT, ICCV 2023)
                            </li>
                            <li>
                                Diffusion Models and Gaussian Flow Matching: Two Sides of the Same Coin (ICLR 2025)
                            </li>
                            <li>
                                Code
                                <a href="https://nn.labml.ai/diffusion/index.html">
                                    [Diffusion Model]
                                </a><br>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="./diffusion_model.ipynb">slides</a></td>
                    <td>数字媒体与虚拟现实：董书承，尹泽凯，田霖，吕伶，曲恃正</td>
                </tr>
                <tr>
                    <td colspan="7" class="schedule_week" align="center" height="28" valign="middle">第 14 周 SAM 调用、LoRA 微调、Hugging Face 介绍与调用</td>
                </tr>
                <tr>
                    <td sdnum="1033;0;@" align="left">2025.12.18, 18:00</td>
                    <td align="left">实训楼 310</td>
                    <td align="left"> SAM 部署与 LoRA 微调</td>
                    <td align="left">\</td>
                    <td align="left">
                        <b>参考资料:</b><br>
                        <ol style="padding-left: 20px;">
                            <li>
                                Code
                                <a href="https://nn.labml.ai/lora/index.html">
                                    [LoRA,
                                </a><br>

                                <a href="https://github.com/facebookresearch/sam2">
                                    facebookresearch SAM2,
                                </a><br>

                                <a href="https://colab.research.google.com/drive/1dI_-HVggxyIwDVoreymviwg6ZOvEHiLS">
                                    NTU Stable Diffusion Fine-tuning homework]
                                </a><br>
                            </li>
                        </ol>
                    </td>
                    <td align="center"><a href="./gan.ipynb">slides</a></td>
                    <td>医学影像计算工程实验室：刘昊辰，脑信息与神经康复实验室：鲁群超</td>
                </tr>
            </tbody>
        </table>
        <p>&nbsp;</p>
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